Data mining is the extraction of knowledge from large datasets using statistical techniques to identify hidden patterns and inform decision-making. It is crucial for companies to gain insights, enhance revenue, and develop marketing strategies, with applications including fraud detection and market research. The data mining process involves steps such as data preparation, model building, and result evaluation.

1. And its Techniques
DATA
MINING

2. • Definition: Data mining is the process of extracting knowledge
or insights from massive amounts of data through statistical
and computational techniques.
• Objective: Finding hidden patterns and relationships in data to
make predictions or informed decisions.
• Importance: Enables companies to gain insights into clientele,
improve revenue, reduce costs, and develop effective
marketing strategies.
Introduction to Data Mining

3. How Data Mining Works
• Process: Examining and evaluating big
data blocks to find significant patterns
and trends.
• Applications: Fraud detection, credit
risk management, spam filtering,
market research, etc.
• Steps:
• Data Gathering and Loading
• Data Organization and Planning
• Custom Application Software
Utilization

4. • Association Rules
• Classification
• Clustering
• Decision Trees
• K-Nearest Neighbor (KNN)
• Neural Networks
• Predictive Analysis
Data Mining Techniques

5. Data Mining
Process
• Understand the
Business
• Understand the Data
• Prepare the Data
• Build the Model
• Evaluate the Results
• Implement Change and
Monitor

6. Thank's For Watching
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